Technique and result test of transformer voire feature extraction under high noise environment

In the whole power system, the importance of the transformer is self-evident, if its fault is bound to cause adverse effects on the whole power system. However, the previous fault diagnosis method is not only time-consuming and laborious, but also has great danger, once it is not handled properly, it may also pose a threat to the life safety of operators. At this time, the transformer fault diagnosis mode based on voice print follows and becomes a big tool for fault diagnosis. However, the surrounding environment of the transformer is changeable and noisy, which will cause interference to the voice print signal and affect the fault diagnosis result. In this regard, this paper will start from this key point and solve this unfavorable factor by using different denoising technologies and feature extraction technologies. The final empirical results show that the improved wavelet threshold technology has the best denoising ability, and the feature parameters obtained under the transformer sound frequency cepstrum coefficient (TFCC) feature extraction technology can better improve the fault diagnosis accuracy. It can solve the problem of low fault diagnosis rate of transformer in different environments, and can be used in practical engineering.